building and cost analysis of an industrial automation

Avrupa Bilim ve Teknoloji Dergisi

Özel Sayı 28, 1-10 , Kasım 2021

© Telif hakkı EJOSAT’a aittir

Araştırma Makalesi

www.ejosat.com ISSN:2148-2683

European Journal of Science and Technology

Special Issue 28, 1-10, November 2021

Copyright © 2021 EJOSAT

Research Article

http://dergipark.gov.tr/ejosat 1

Building and Cost Analysis of an Industrial Automation System

using Industrial Robots and PLC Integration*

Enes Efe1†, Muciz Ozcan2, Huseyin Hakli3

1* Hitit University, Department of Electrical and Electronics Engineering,Corum, Turkey, (ORCID ID 0000-0002-6136-6140), [email protected] 2 Necmettin Erbakan University, Department of EEE, Konya, Turkey, (ORCID ID 0000-0001-5277-6650) [email protected]

3 Necmettin Erbakan Univertsity, Department of Computer Engineering, Konya, Turkey, (ORCID ID 0000-0001-5019-071X) [email protected]

(1st International Conference on Applied Engineering and Natural Sciences ICAENS 2021, November 1-3, 2021)

(DOI: 10.31590/ejosat.972290)

ATIF/REFERENCE: Efe, E., Ozcan, M. & Haklı, H. (2021). Building and Cost Analysis of an Industrial Automation System using

Industrial Robots and Programmable Logic Controller Integration. European Journal of Science and Technology, (28), 1-10.

Abstract

Technology rapidly advances on a daily basis and the resulting changes can provide numerousbenefits for manufacturing methods and

machines. Manufacturers who are able to swiftly embrace thesedevelopments can increase their manufacturing output, thereby

boosting profitability and gainingcompetitive advantages over their rivals. However, the cost savings which result from new

innovationscan vary, depending on the manufacturing model. Consequently, manufacturers need to conduct accurateanalyses for

appropriate manufacturing methods in order to ensure that new changes are cost-effective.Nowadays, the use of industrial automation

systems is gaining popularity as a method of increasingprofitability for mass production, and these systems utilize control systems,

such as industrial robots andprogrammable logic controllers. The use of these elements in the manufacturing process not onlyprovides

quality and flexible production methods, which are indispensable considerations, but alsoconserves human effort. The aim of this

study was to minimize the cost of a factory-installed industrialautomation system, which produced globe valves with side couplings,

through the combined use ofindustrial robots and programmable logic controllers. While calculating returns from the installed

system,the differential evolution algorithm was used to predict future unit prices of electricity, and it wasdetermined that the cost of

investment would be recovered after a maximum of 2.5 years and that currentyearly production would increase fourfold.

Keywords: Industrial automation, Programmable logic controller, Industrial robots, Differential evolution algorithm, Prediction.

Endüstriyel Robot ve PLC Entegrasyonuyla Talaşlı İmalat Üretim

İşleminin Gerçekleştirilmesi

Öz

Üreticiler üretim şekillerini değiştirmeden önce doğru analizler yaparak kendilerine en uygun üretim yöntemini seçmeleri

gerekmektedir. Bu çalışmada yan rakorlu küresel vana üretilen bir fabrikada Endüstriyel Otomasyon Sistemi kurulmuş ve kurulan bu

sistemin maliyeti Endüstriyel Robot ve PLC beraber kullanılarak en aza indirilmesi hedeflenmiştir. Üretim yönteminde Endüstriyel

Robotun kullanılması ve mevcut sistemde ki üretimde bir takım değişiklikler yapılmasıyla esnek üretim sağlanmış ve üretimin

aksamaması için bazı tedbirler alınmıştır. Kurulan sistem maliyetinin geri dönüşüm süreci hesaplanmasında Diferansiyel Evrim

Algoritmasından yararlanılarak gelecekteki elektrik birim fiyatları tahmin edilmiştir. Bu çalışmada, yapılan yatırımın en fazla 2,5 yıl

içerisinde geri döneceği ve mevcut yıllık üretim miktarının da yaklaşık 4 kat artacağı tespit edilmiştir.

Anahtar Kelimeler: Endüstriyel Otomasyon, PLC, Endüstriyel Robot, Diferansiyel Evrim Algoritması.

* This paper was produced from the master's thesis of the first author † Corresponding Author: [email protected]

http://dergipark.gov.tr/ejosat

mailto:[email protected]





e-ISSN: 2148-2683 2

1. Introduction

As technology advances apace, new innovations have a critical

effect on our lives. Manufacturing quality and the availability of

cheap products have become inevitable necessities in today’s

global markets. The use of automation technologies has made it

is possible to mass-produce items and lower production costs [1].

Automation is defined as the automatic control of a tool, process,

or system as a result of observation, decision making, and the

ability to effect changes via mechanical or electronic devices,

rather than human interaction [2]. An entire task is shared by

humans and machines, and the sharing ratio of this activity

effectively determines the level of automation. If human power

predominates, the resulting system is said to be semi-automated;

if machine power is dominant, this is known as full automation

[3]. Industrial automation (IA), which uses modern techniques

and applications in manufacturing, is an impressive

manufacturing strategy that leads to ongoing competition

between rival companies [4]. These systems are capable of

replacing human power in virtually any business sector and

utilize vital control systems, such as programmable logic

controllers (PLC), industrial robots, computers and information

technologies. This strategy enables quality and flexibility to be

increased within manufacturing operations, and, on inspecting

the manufacturing processes of developed countries, it becomes

immediately obvious when automation systems have been

widely deployed.

Industrial robots, which are among the most critical elements

of industrial automation systems, continue to grow in importance

on a daily basis [5]. Japan was the first country to use robots in

industry and, at the time, their introduction brought concerns that

unemployment would rise. However, their widening usage has

removed any doubts that this would happen and has, in fact, led

to many new lines of work, with the result that unemployment

has decreased substantially [6]. Today, they are most frequently

used in working environments where there is a risk to human life

from hazardous conditions, resulting from high temperatures,

mechanical vibrations, chemicals, and nuclear energy [7].

The PLC is another important element of industrial automation

systems. Intended to replace relay command circuits, the PLC

was so named because it was only able to perform basic logic

operations when first introduced. Firms such as Allen Bradley,

General Electric, Siemens, and Westinghouse produced the first

medium-cost and high-performance PLCs, allowing this type of

controller to be applied in industry [3]. As Toshiba, Mitsubishi,

and Omron developed low-cost, high-performance devices, their

use in industrial automation systems became even more

widespread [8]. PLCs have many attributes including, but not

limited to, flexibility, reliability, ease of expansion, and low

power consumption. It is possible to control alterations and

enlarge the system solely by changing the PLC software [9].

Various industrial automation studies have been documented

in which PLCs and industrial robots have been used. In 2007,

Niola et. al. examined the possibility of using a video system to

plan a robot’s trajectory, and this was achieved by controlling

robots with a computer mouse from a personal computer (PC)

monitor [10]. Dong and Kuang’s 2013 study analyzed the

communication signals between a Mitsubishi PLC and

GlaxoSmithKline robot [11]. In 2017, Stückelmaier et al.

presented a method of gradually increasing the trajectory

accuracy of industrial robots and examined dynamic modeling,

definition, and kinematic calibration [12]. Jeong et al. published

a study, in 2017, that discussed the software architecture of an

integrated PLC robot, which played a key role in industrial smart

systems [13]. Chen and Dong conducted studies to increase the

accuracy and efficiency of robotic engraving and examined the

feasibility of developing engraving systems that are capable of

carrying out tasks that were previously thought to be the preserve

of computer numerical control (CNC) machines [14].

In developed countries, engineering services are usually billed

on an hourly basis. However, this is not the case in developing

countries, such as Turkey, where remittance usually occurs at

monthly intervals. In addition, advanced robots are more

expensive compared to basic machines, and this results in

additional costs for developing countries. Consequently,

developed countries are able to use high-end industrial robots,

that do not require additional engineering services and are

capable of using PLC software, rather than having to procure

further control systems. However, installation costs can be

lowered by the use of a basic robot operating in conjunction with

a PLC, and a number of studies have been documented that focus

on reducing this expenditure.

This study was conducted in an active and physically-

accommodating plant and produced statistical data, which was

subsequently evaluated. In order to accurately calculate the cost

of the installed system and determine the payback period, it was

necessary to establish the price of the total electrical energy that

the system would expend. This was determined by the use of a

differential evolution (DE) algorithm to successfully predict

future unit electricity prices, thereby allowing the total electricity

bill for the system to be calculated. The DE algorithm is suitable

for a range of applications in the electrical energy sector and can,

for example, be used to determine load estimates, which are a

crucial consideration in this field. Eke successfully estimated

medium-term load using the DE algorithm [15], and Wang et al.

used the DE algorithm to predict electrical energy consumption

[16].

This study evaluated an automation system, consisting of an

industrial robot and PLC, which was installed in a factory that

manufactures side coupling globe valves (SCGV). The findings

from this exercise were assessed and the costs of the system were

compared with those that were incurred prior to installation. One

of the most important aspects of this new system was the PLC,

which, in combination with the industrial robot, enabled fast

production. In addition, this study allowed the introduction of

new techniques to calculate costs and determine the payback

period.

This paper is structured as follows: Sections 2 are concerned

with the installation of the industrial automation system and

application of the proposed method; and Section 3-4 examines

the experimental results which were obtained for the installed

system.

2. Material and Method

This study focused on the use of an industrial automation

system for the manufacture of valves with side couplings. Two

CNC machines, a 6-axis industrial robot, and a PLC were

provided for this purpose, and communications between these

elements were achieved by the use of digital input-output signals.


e-ISSN: 2148-2683 3

2.1. Industrial Robot and PLC

An industrial robot is defined, according to ISO 8373, as a

manipulator with three or more axes, which is programmable,

multipurpose, and can be either wheeled or stationary [17]. The

mobility of this manipulator varies according to its number of

axes and features, and electrical, hydraulic, or pneumatic driver

systems are used to power its joint movements. An industrial

robot essentially comprises the manipulator, a teach pendant, and

controller, while elements that remain outside the system

boundaries of the robot are termed peripherals. The end effector,

in this case, a standard pneumatic gripper, is used to grab the

working parts (Figure 1). Other peripherals include sensors,

machines, conveyor belts, and security equipment, etc. The

controller ensures that the industrial robot works in harmony

with the peripheral devices and performs the desired movements

[18].

Fig. 1 The Standard Pneumatic Gripper Used as an End

Effector in this Study.

PLCs have a microprocessor base, receive information from

detectors in the field, and process this information as instructed.

Consequently, they are able to control remote instrumentation

and can be used in industrial automation systems to execute

command and control mechanisms. In addition, PLCs are

industrial computers that are equipped with input and output

capabilities that enable them to communicate with separate

devices and work in combination with SCADA [19]. The PLC

examined in this study allowed the actions of the industrial robot

and CNC to be synchronized, thereby allowing control of the

hardware elements in the system.

2.2. DE Algorithm

The DE Algorithm was proposed by Storn and Price in 1995

and is a population-based method that uses operators resembling

those in genetic instructions, such as mutation, selection, and

crossing [20]. The DE Algorithm uses the following parameters:

population size (Np), scaling factor (F), and crossing ratio (Cr).

The principal steps of the DE Algorithm are:

Step 1: Determination of the initial population.

Step 2: Evaluation of the initial population.

Step 3: Mutation.

Mutation is a process that adds the scaled difference of two

random individuals to another individual that has been randomly

selected from the population.

𝑣𝑚,𝑡+1 = 𝑥𝑟3,𝑡 + 𝐹 × (𝑥𝑟1,𝑡 − 𝑥𝑟2,𝑡) (1)

In Equation 1, 𝑣𝑚,𝑡+1 represents the mutated individual, while

𝑥𝑟1,𝑡 , 𝑥𝑟2,𝑡, and 𝑥𝑟3,𝑡 are randomly selected individuals from the

population 𝑥𝑟1≠ 𝑥𝑟2

≠ 𝑥𝑟3≠ 𝑥𝑖).

Step 4: Crossing.

The value of 𝐶𝑟 determines which genes are taken from the

new individual, due to the mutation. If a randomly-generated

value between 0 and 1 is less than 𝐶𝑟, it is chosen from 𝑛𝑗,𝑖,𝐺+1;

if it is greater than C_r, it is selected from the current vector. The

aim of these operations is to ensure that a previously specified

ratio of genes are taken from the mutated individual. The

mathematical expression for the crossing method is:

𝑥𝑗,𝑢,𝑡+1 = {𝑥𝑗,𝑖,𝑡 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝑥𝑗,𝑚,𝑡+1 𝑖𝑓 𝑟𝑎𝑛𝑑[0,1]≤𝐶𝑟 𝑜𝑟 𝑗=𝑗𝑟𝑎𝑛𝑑 (2)

In Equation 2, xu,t+1 represents the candidate individual after

crossing, and j is the current gene index. If none of the values

acquired for any gene are less than Cr, the current individual

remains unchanged. In order to circumvent this situation, a

randomly-selected gene (jrand) is updated to ensure at least one

gene mutation occurs.

Step 5: Choice.

A choice is made in favor of the candidate individual or

current individual that best fits the criteria. This practice is

generally known as the greedy-choice method. A comparison is

made between the current individual’s purpose function

performance and the candidate’s solution.

Step 6: Stopping Criterion.

Steps 3, 4, and 5 are repeated sequentially until the stopping

criterion is satisfied. Otherwise, the best solution is reported and

the algorithm terminates.

2.3. Installation of the Industrial Automation System

The components of the industrial automation system

installed during this study are shown in Figure 2. The system

comprised two CNC machines, a handling station, an industrial

robot, and PLC. The CNC machines were positioned directly

opposite each other, with the robot installed between them, and

the handling station was placed in front of the robot. The PLC

controlled the hardware elements of the system and was in

constant communication with the industrial robot. In addition to

ensuring that the industrial robot and the CNCs worked in

synchronization, the PLC controlled the operation of the

handling station, while the industrial robot commanded the CNC

machines. A program was written and uploaded to the PLC, in

order to execute the necessary control operations.

Fig. 2 Illustration of the Control Components of the System.

Two grippers were used to exchange processed and

unprocessed parts. The first gripper carried the completed SCGV


e-ISSN: 2148-2683 4

away from the CNC, while the second gripper transported the

unprocessed part to the CNC (Figure 3).

(a) Unprocessed Part.

(b) Processed Part.

Fig. 3

Having selected appropriate grippers, it was necessary to

design a handling station that was suitable for use with the robot

arm. Processed parts were carried to the conveyor by the robot.

The exchange of parts between the handling station and

industrial robot was made possible by the use of optical

proximity sensors, which were keyed at a frequency of 1000 Hz.

When the part had been completed, the sensors sent a ready

signal to the robot, which seized it using the gripper. Figure 4

depicts the position of the sensors on the handling station, while

Figure 5 shows the use of the gripper to seize a part.

Figure 4. Handling Station

Sensors.

Figure 5. Gripper

Receiving a Part.

In order to ensure that the actions of the handling station and

CNC machines were coordinated, it was necessary to use a PLC

with a sufficient number of inputs and outputs. The PLC was

required to control the handling station, CNC gates, and mirrors,

and the manufacturer’s proprietary ladder programming language

was used to ensure this functionality was satisfied. In addition to

being operated via the industrial robot, the CNC machine gates

and mirrors were also controlled by the PLC. Manual operation

was therefore possible when the industrial robot was switched

off. When the system starts up, the industrial robot initially takes

the unprocessed part from the handling station (Figure 6 (a)) and

then hands it to the CNC for processing, while simultaneously

taking the completed part away from the machine (Figure 6 (b) -

(c)). Lastly, the robot places the processed part on the conveyor.

(a)

(b)

(c)

(d)

Fig. 6 Industrial Robot Actions.

Control of the CNC machine’s gates was switched from

manual to automatic using dual pneumatic valves, a 40 x 40 x

600 mm piston, and magnetic sensors, which operated at a

switching frequency of 5000 Hz (Figure 7).

(a)

(b)

Fig. 7 (a) Piston. (b) Dual Pneumatic Valve.

Finally the industrial robot was programmed with its integrated

programming language, which resembled C, to ensure system

stability.

2.4. Electricity Price Prediction Using the DE Algorithm

In order to observe the system’s efficiency and determine the

payback period, it was necessary to estimate the unit price of

electricity in the following years. Since electricity prices depend

on a multitude of parameters, they can be highly variable over a

period of time. It was therefore necessary to adopt the use of

robust and reliable methods, through the use of smart systems, to

provide suitable forecasts. Although there are no documented

studies concerning the calculation of unit electricity prices,

numerous enquiries have been carried out into the estimation of

future energy demand. Many nature-inspired algorithms have

been used for this purpose and these include a number of

computational methods, such as the genetic algorithm [21, 22],

particle swarm optimization [23, 24], ant colony optimization

[25], artificial bee colony optimization [26], the DE algorithm

[27], and various hybrid techniques [28]. Studies have also been

undertaken with regard to the estimation of electricity generation

and consumption [29-31]. This study adopted the use of the DE

algorithm, which, in addition to offering efficient performance

and being easy to apply, was based on the work of Beskirli et al.

[27], who proposed its use for energy demand estimation in

preference to other nature-inspired methods.

Four economic criteria were considered when estimating

energy demand: gross domestic product (GDP), population, and

values of imports and exports. [23-27]. These parameters,

together with electricity generation and consumption, directly

influence unit electricity prices. Table 1 contains values for these

6 criteria between 1995 and 2015 [29].


e-ISSN: 2148-2683 5

Table 1. Unit electricity price (kWh), GDP, population, values of imports and exports, electricity production and electricity

consumption for Turkey between 1995 and 2015.

Name and number of variables

1 2 3 4 5 6

Year Unit Price

(kWh)

GDP

($10^9)

Population

(10^6)

Import

($10^9)

Export

($10^9)

Electricity

production

(10^9)

Electricity

consumption

(10^9)

1995 0.20 168.08 58.48 35.71 21.64 86.25 67.39

1996 0.36 181.07 59.42 43.63 23.22 94.86 74.16

1997 0.71 188.73 60.37 48.56 26.26 103.30 81.89

1998 1.14 270.94 61.32 45.92 26.97 111.02 87.71

1999 1.94 247.54 62.28 40.67 26.59 116.44 91.20

2000 3.43 265.38 63.24 54.50 27.78 124.92 98.30

2001 4.72 196.73 64.19 41.40 31.33 122.73 97.07

2002 10.62 230.49 65.14 51.55 36.06 129.40 102.95

2003 12.00 304.90 66.08 69.34 47.25 140.58 111.77

2004 11.22 390.38 67.00 97.54 63.17 150.70 121.14

2005 11.22 481.49 67.90 116.77 73.48 161.96 130.26

2006 10.00 526.42 68.76 139.58 85.54 176.30 143.07

2007 9.39 648.75 69.59 170.06 107.27 191.56 155.14

2008 14.37 742.09 70.44 201.96 132.03 198.42 161.95

2009 14.15 616.70 71.33 140.93 102.14 194.81 156.89

2010 15.31 731.60 72.32 185.54 113.88 211.21 172.05

2011 15.31 773.97 73.40 240.84 134.91 229.40 186.10

2012 15.81 786.28 74.56 236.54 152.47 239.50 194.92

2013 18.16 823.04 75.78 251.66 151.81 240.15 198.05

2014 18.16 799.36 77.03 242.17 157.62 251.96 207.38

2015 19.28 719.62 78.27 207.23 143.84 261.78 217.31

Examination of Table 1 indicates that, although electricity

generation and consumption have steadily increased over the

years, there are periods of time when electricity unit prices have

stayed relatively constant, or even slightly declined, prior to

subsequent increases. In general, GDP and the values of imports

have risen steadily between 1995 and 2013, although they have

fallen in the last two years of the period considered.

Two separate models, one linear, the other quadratic, were

implemented to estimate energy or electricity demand [23, 25-

27]. Of these, the quadratic model proved to be much more

efficient and was preferred for this study. The quadratic equation

for four variables can be expressed as follows:

𝐸𝑞𝑢𝑎𝑑𝑟𝑎𝑡𝑖𝑐 = 𝑤1 + 𝑤2𝑋1 + 𝑤3𝑋2 + 𝑤4𝑋3 + 𝑤5𝑋4 +

𝑤6𝑋1𝑋2 + 𝑤7𝑋1𝑋3 + 𝑤8𝑋1𝑋4 + 𝑤9𝑋2𝑋3 + 𝑤10𝑋2𝑋4 +

𝑤11𝑋3𝑋4 + 𝑤12𝑋12 + 𝑤13𝑋2

2 + 𝑤14𝑋32 + 𝑤15𝑋4

2 (3)

The parameters 𝑋1, 𝑋2, 𝑋3, 𝑋4 represent GDP, population,

values of imports, and values of exports, respectively, while 𝑤1,

𝑤15 are their corresponding weights. Equation 3 is formulated

for 4 variables, and if this number increases, the formula must be

changed accordingly. For 4 variables, there are 15 weights (also

the dimensions of the problem); when there are 5 variables there

are 21 weights; and 6 variables require 28 weights. The accuracy

of the prediction is verified by the purpose function shown in

Equation 4.

𝑚𝑖𝑛𝑓(𝑣) = ∑ (𝐸𝑟𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑅

𝑟=1 − 𝐸𝑟𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑

)2 (4)

𝐸𝑟𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 and 𝐸𝑟

𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 are the observed and predicted unit

electricity prices, respectively, and R is the year in which the

observation occurs. The main objective is to find the weights

which correspond to the 𝑚𝑖𝑛𝑓(𝑣) value for each year [27]. The

smaller 𝑓(𝑣) is, the more accurate the estimate for a given year

is.

3. Results

In this study, experimental testing was carried out using a PC

equipped with an i5-6400 central processing unit (CPU), an

AMD Radeon R7-200 graphics processing unit (GPU), and 8

gigabytes (GB) of random-access memory (RAM). The

Industrial Automation System was realized in firm facilities. The

experimental results were separated into three parts; an


e-ISSN: 2148-2683 6

evaluation of the utility of the system, unit electricity price

estimates, and cost analysis and payback period calculations.

3.1. Analysis of the Industrial Automation System Output

A significant increase in production was noted after the

industrial robot and PLC had been installed. Prior to automation,

the daily production output, between 8 a.m. and 6 p.m., was an

average of 400 units, while after installation the output had risen

to an average quantity of 585 units. If the system was allowed to

run round the clock, this output jumped to around 1400 units.

Previously, daily production was limited to approximately 8

hours, after the workers’ lunch breaks and recess times had been

taken into account. Following automation, continuous production

was possible on a 24-hour basis, and this was the most

significant benefit to result from installation of the system.

The system provides an additional advantage in that the PLC

can allow manual production, if required. Machining is only

possible with the use of the industrial robot; however, manual

production is only possible if the PLC and robot are active.

3.2. Future Projections of Unit Electricity Price

The data presented in Table 1, over a 21 year period, was used

for the estimation of electricity unit prices, using the DE

algorithm. In this analysis, F was taken to be 0.5, while Cr and

Np were set to values of 0.9 and 100, respectively. The

maximum number of function evaluations was specified as

5x105. The variables GDP, population, and values of imports

and exports were initially tested as stand-alone criteria;

electricity generation and consumption were then tested in

combination with the other parameters (Table 2). The quadratic

form given in Equation 3 was rearranged in accordance with the

number of variables used.

The DE algorithm was run using variables that were chosen for

4 different scenarios, and, after 10 trials, the best results were

selected for use in this study. Table 2 gives the weights and

errors for the 4 variables used.

Table 2. Comparisons of Coefficients and Relative Errors.

Selected Variables

Coefficients 1,2,3,4 1,2,3,4,5 1,2,3,4,6 1,2,3,4,5,6

w1 10.00000 -9.17532 9.62941 -8.48483

w2 -0.24023 0.09704 3.32920 -2.61183

w3 -1.29403 -9.99305 1.60644 9.02448

w4 -3.00539 2.19926 -4.12544 -8.70575

w5 6.02331 -4.09795 -6.21791 -1.76599

w6 0.00453 3.31954 -6.13156 9.94205

w7 0.00122 -0.00483 -0.08804 -7.33863

w8 0.00204 -0.01348 -0.00783 -0.01581

w9 0.04775 0.00536 -0.02306 -0.03232

w10 -0.09946 -0.00096 0.02342 0.21898

w11 -0.00991 -0.03412 0.15556 0.49497

w12 -0.00041 0.14561 0.20481 -0.54486

w13 0.01937 -0.15470 0.04591 -0.29004

w14 -0.00037 0.06002 0.04389 0.43933

w15 0.00681 0.01262 -0.07044 1.65158

w16 - -0.05923 -0.05802 -1.34516

w17 - 0.00168 0.00228 0.58050

w18 - 0.24909 0.01065 1.91664

w19 - -0.00033 0.01433 -2.07848

w20 - -0.03886 0.03802 -2.73528

w21 - 0.03174 0.01783 2.86035

w22 - - - -1.90207

w23 - - - -0.01904

w24 - - - -0.55196

w25 - - - -0.17802

w26 - - - -0.85209

w27 - - - -0.38560

w28 - - - 2.64912

Error Rate 33.21 9.79 21.83 3016.10

Examination of Table 2 reveals that the best electric unit price

estimation, with a 9.79 error rate, resulted from the combination

of variables 1, 2, 3, 4, and 5. When electricity generation and

consumption were both included in the calculations, the error

rate for the estimate became significantly higher in relation to

other conditions. Incremental increases in the error value

corresponded to higher fitting values, which are the product of a

square function [27]. Future projections relied on the

combination of GDP, population, values of imports, and values

of exports.

Figure 8 compares the estimates obtained for variables 1, 2, 3,

4, and 5, with the values observed between 1995 and 2015. The

results show that, in general, the estimations were very close to

the actual values, and it can be concluded that the proposed

model was reliable and fit for purpose. This is particularly true

when examining the findings for 2000, 2007, and 2009; although

some disparity is evident for 2002 and 2003.


e-ISSN: 2148-2683 7

Fig. 8

Comparison of Observed and Estimated Electricity Unit Prices.

Three different scenarios were prepared for the estimation of

electricity unit prices in Turkey, between 2016 and 2035.

Scenario 1: Between 2016-2035, the following were assumed:

a mean GDP growth rate of 3%, a population growth rate of 1%,

an import growth rate of 3%, and a 5% increase in electricity

production and values of exports.




production and values of exports.




production and values of exports. Table 3 contains future

projections that were obtained for each scenario, using the

weight values acquired from the DE algorithm. These scenarios

were determined by considering low, normal, and high levels of

increase in the appropriate variables. Examination of the findings

reveals that Scenario 1 resulted in the highest level of agreement

between the estimations and actual figures, from 2016 to 2018.

The rapid increase in unit price observed for Scenario 2 and less

inflationary trends of Scenarios 1 and 3 are illustrated in Figure

9. It can be seen that after 2022, the estimates obtained for

Scenario 3 were initially smaller than those of the other data sets;

after 2030, however, they quickly increased and went on to

exceed the values derived for Scenario 1. The results show that

the estimated figures for Scenarios 2 and 3 continued to increase

over time, while the values obtained for Scenario 1 fell over the

last two years of the time frame.

Fig. 9 Future Projections of Electricity Unit Price for Scenarios 1-3.

0

2

4

6

8

10

12

14

16

18

20

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

20

15

Ele

ctri

city

Un

it P

rice

Years

Observed

Estimated

1030507090

110130150170190210230250270290310330350

2016 2018 2020 2022 2024 2026 2028 2030 2032 2034

Scenario 1 Scenario 2


e-ISSN: 2148-2683 8

3.3. Systems Costs and Profit Analysis

Future projections of the electricity unit price were used to

carry out a cost and profit analysis, and the following tariffs were

added to these values: a 40% distribution charge for unilateral

industry groups, a 1% energy fund, a 5% municipal consumption

tax (MCT), and 18% value added tax (VAT). Table 3 shows the

resulting estimates for all three scenarios.

Table 3. Electricity Prices Estimates Including Tax (Turkish

kurus).

Scenarios

Year 1 2 3

2019 39.36 64.54 105.85

2020 42.94 70.42 115.49

2021 47.23 77.46 127.04

2022 52.23 85.66 140.48

2023 57.89 94.93 155.69

2024 64.14 105.19 172.52

2025 70.89 116.27 190.68

2026 78.00 127.93 209.80

2027 85.28 139.86 229.37

2028 92.48 151.67 248.73

2029 99.28 162.83 267.04

2030 105.30 172.69 283.21

2031 110.04 180.46 295.95

2032 112.89 185.14 303.64

2033 113.14 185.56 304.31

2034 109.92 180.26 295.63

2035 102.16 167.54 274.76

Two different modes of operation were examined during this

study. In the first of these (Mode a), it was assumed that the new

system would work continuously for 24 hours; in the second

mode (Mode b), it was assumed that work would only take place

between the hours of 8 a.m. and 6 p.m. The total energy costs of

the system were calculated for both operating modes, using the

previous cost estimates with taxes (Table 4). In addition, the

ticket prices of the control elements of the system were included,

and the average power consumption of these components was

taken as 5.5 kW and multiplied by the electricity prices including

tax. The total number of working days was assumed to be 260,

allowing for weekends, and the results were multiplied by this

figure in order to calculate the annual energy costs of the system.

Table 4. Total Energy Costs of the System for Various

Scenarios and Operating Modes (103 Turkish lira).

Scenarios/ Operating Modes

1 2 3

Year a b a b a b

2019 13.51 5.63 22.15 9.23 36.33 15.14

2020 14.74 6.14 24.17 10.07 39.63 16.51

2021 16.21 6.75 26.59 11.08 43.60 18.17

2022 17.93 7.47 29.40 12.25 48.21 20.09

2023 19.87 8.28 32.58 13.58 53.43 22.26

2024 22.01 9.17 36.10 15.04 59.21 24.67

2025 24.33 10.14 39.90 16.63 65.44 27.27

2026 26.77 11.15 43.90 18.29 72.00 30.00

2027 29.27 12.20 48.00 20.00 78.72 32.80

2028 31.74 13.22 52.05 21.69 85.37 35.57

2029 34.07 14.20 55.88 23.28 91.65 38.19

2030 36.14 15.06 59.27 24.69 97.20 40.50

2031 37.76 15.74 61.93 25.81 101.57 42.32

2032 38.74 16.14 63.54 26.48 104.21 43.42

2033 38.83 16.18 63.68 26.53 104.44 43.52

2034 37.72 15.72 61.87 25.78 101.46 42.27

2035 35.06 14.61 57.50 23.96 94.30 39.29

It was assumed that workers would be paid the minimum

wage. Since accurate forecasts for the inflation rate were critical

for reliable estimates, appropriate data was obtained from the

Central Bank of Turkey. After installation, labor costs were

largely replaced by the energy costs of the system, and the

difference between them was multiplied by 260 to give the

profits shown.

The price increases for SCGVs were determined in accordance

with the inflation rate forecasts. SCGV unit prices were

multiplied by the average daily production figure of 400 and

number of working days (260) to obtain the annual income prior

to installation. The corresponding revenue after installation was

calculated in a similar fashion, and daily production estimates of

1400 and 585 units were assumed for Modes a and b,

respectively.

Table 5 presents the cumulative total profit for each scenario

and operating mode over the period in question. It should be

noted that the manufacturer paid an initial system installation

cost of 42,000 euros.


e-ISSN: 2148-2683 9

Table 5. Cumulative Total Profit After Installation of the System (103 Turkish lira).

Scenarios

1 2 3

Years a b a b a b

2019 403.40 93.43 394.75 89.82 380.57 83.92

2020 847.03 196.02 828.96 188.49 799.31 176.14

2021 1334.62 308.47 1306.17 296.61 1259.51 277.17

2022 1870.22 431.47 1830.30 414.83 1764.83 387.55

2023 2458.33 565.80 2405.70 543.87 2319.37 507.90

2024 3104.08 712.50 3037.36 684.69 2927.93 639.10

2025 3813.16 872.67 3730.86 838.38 3595.89 782.14

2026 4591.86 1047.59 4492.43 1006.16 4329.36 938.22

2027 5447.20 1238.67 5329.04 1189.43 5135.25 1108.69

2028 6386.96 1447.47 6248.49 1389.77 6021.39 1295.15

2029 7419.85 1675.81 7259.57 1609.03 6996.71 1499.50

2030 8555.56 1925.74 8372.15 1849.32 8071.35 1723.99

2031 9804.89 2199.54 9597.31 2113.05 9256.87 1971.20

2032 11179.85 2499.80 10947.47 2402.98 10566.37 2244.19

2033 12693.86 2829.43 12436.62 2722.25 12014.77 2546.47

2034 14361.85 3191.69 14080.47 3074.45 13619.02 2882.17

2035 16200.49 3590.27 15896.68 3463.68 15398.43 3256.08 Initial installation cost was 42,000 euros (222,306 TL, according to exchange rate of 01.06.18).

Figure 10 shows the profit graphs for the scenarios and

operating modes in question. It can be seen that if the automated

system commenced operation in 2019, it would become

profitable within 2-3 years, assuming 8 a.m. to 6 p.m. working.

However, if the system was operated on a 24 hour basis, it would

become profitable within the first year. Although theoretically

possible, producers prefer to avoid continuous production due to

the attendant reduction in machine service life.

Fig. 10 Profit Graph of the System.

4. Discussion and Conclusion

This study considered the installation of an industrial

automation system which produced SCGVs. One of the

innovations that were done in this study is that the Industrial

Automation System computes the time to return to profitability

by using artificial intelligence techniques. In contrast to many

other systems, a PLC was used in addition to an industrial robot

and, in order to accurately calculate the costs of the installed

system to the producer, a DE algorithm was used to estimate

future electricity unit prices in Turkey. Thus the total energy cost

was calculated and compared with the labor costs prior to

installation. In the light of this study, the system was expected to

become profitable within 2.5 years and return considerable gains

thereafter.

The use of a PLC provides significant advantages, namely: the

ability of the system to operate manually, providing continuous

production, and the option to procure cheaper robots without

additional features. Furthermore, the resulting simplicity of the

robot software increases the usability of the system. In summary,

the installation of this automated system resulted in increased

production and higher levels of efficiency and flexibility.

In the future, it may be possible to achieve greater efficiency

by reducing energy expenditure. The electric energy used by the

system could be supplied from renewable energy sources, and

the system could be made more economical. Moreover, the use

of a wireless communication feature could increase the usability

of the system by enabling control over a remote computer.

References

1. Yücel, İ.H., Sanayide Robot Teknolojisi. Uygulaması Ve

Önemi, DPT Yayınları, Ankara, Aralık, 1991.

2. Sheridan, T.B., Telerobotics, automation, and human

supervisory control. 1992: MIT press.

3. Özcan, M., Otomasyon sistemlerinde PLC uygulamaları.

2004: Atlas Yayın dağıtım.


e-ISSN: 2148-2683 10

4. Acharya, V., S.K. Sharma, and S. Kumar Gupta, Analyzing

the factors in industrial automation using analytic hierarchy

process. Computers & Electrical Engineering, 2017.

5. Çengelci, B. and H. Çimen, Endüstriyel robotlar. Makine

Teknolojileri Elektronik Dergisi, 2005. 2(2): p. 69-78.

6. Peşkircioğlu, N., Otomasyon ve Entegre Kalite Kontrolu.

Verimlilik Dergisi, (15): p. 19-40.

7. De Silva, D., Reactions to Robots, Engineering. 1987, April.

8. Kurtulan, S., PLC ile endüstriyel otomasyon. 2001: Birsen

Yayınevi.

9. Rehg, J.A. and G.J. Sartori, Programmable logic controllers.

2009, Upper Saddle River, N.J.: Pearson Prentice Hall.

10. Niola, V., C. Rossi, and S. Savino, Vision system for

industrial robots path planning. International journal of

Mechanics and control, 2007. 8(1): p. 35-45.

11. CHEN, D.-q. and Y.-c. KUANG, Communication between

Mitsubishi PLC and GSK Industrial Robot. Mechanical

Engineer, 2013. 4: p. 023.

12. Stückelmaier, P., M. Grotjahn, and C. Fräger. Iterative

improvement of path accuracy of industrial robots using

external measurements. in Advanced Intelligent

Mechatronics (AIM), 2017 IEEE International Conference

on. 2017. IEEE.

13. Jeong, H.S., et al. Design of SW architecture for PLC

integrated robot. in Ubiquitous Robots and Ambient

Intelligence (URAI), 2017 14th International Conference on.

2017. IEEE.

14. Chen, Y. and F. Dong, Robot machining: recent

development and future research issues. The International

Journal of Advanced Manufacturing Technology, 2013.

66(9-12): p. 1489-1497.

15. Eke, İ., Diferansiyel evrim algoritması destekli yapay sinir

ağı ile orta dönem yük tahmini. Uluslararası Mühendislik

Araştırma ve Geliştirme Dergisi, 2011. 3(1): p. 28-32.

16. Wang, L., et al., Effective electricity energy consumption

forecasting using echo state network improved by

differential evolution algorithm. Energy, 2018. 153: p. 801-

815.

17. Standard, I., 8373: 1994. Manipulating industrial robots–

Vocabulary.

18. Dişlitaş, S., Endüstriyel Robot Programlama. Baskı,

Endüstriyel Robot Programlama Eğitimi ile Mesleki ve

Teknik Eğitim Güçlendirilmesi (ERPE-METEG) Projesi,

Çorum, 2015.

19. Mirzaoğlu, İ. and M. Sarıtaş, PLC VE SCADA Kullanarak

Bir İrmik Üretim Sisteminin Otomasyonu. 2008.

20. Storn, R. and K. Price, Differential evolution-A simple and

efficient adaptive scheme for global optimization over

continuous spaces [R]. Berkeley: ICSI, 1995.

21. Canyurt, O.E., et al., Energy demand estimation based on

two-different genetic algorithm approaches. Energy Sources,

2004. 26(14): p. 1313-1320.

22. Ceylan, H. and H.K. Ozturk, Estimating energy demand of

Turkey based on economic indicators using genetic

algorithm approach. Energy Conversion and Management,

2004. 45(15-16): p. 2525-2537.

23. Unler, A., Improvement of energy demand forecasts using

swarm intelligence: The case of Turkey with projections to

2025. Energy Policy, 2008. 36(6): p. 1937-1944.

24. Gulcu, S. and H. Kodaz, The estimation of the electricity

energy demand using particle swarm optimization

algorithm: A case study of Turkey. 8th International

Conference on Advances in Information Technology, 2017.

111: p. 64-70.

25. Toksari, M.D., Estimating the net electricity energy

generation and demand using the ant colony optimization

approach: Case of Turkey. Energy Policy, 2009. 37(3): p.

1181-1187.

26. Sonmez, M., A.P. Akgungor, and S. Bektas, Estimating

transportation energy demand in Turkey using the artificial

bee colony algorithm. Energy, 2017. 122: p. 301-310.

27. Beskirli, M., H. Hakli, and H. Kodaz, The energy demand

estimation for Turkey using differential evolution algorithm.

Sadhana-Academy Proceedings in Engineering Sciences,

2017. 42(10): p. 1705-1715.

28. Kiran, M.S., et al., A novel hybrid approach based on

Particle Swarm Optimization and Ant Colony Algorithm to

forecast energy demand of Turkey. Energy Conversion and

Management, 2012. 53(1): p. 75-83.

29. Yumurtaci, Z. and E. Asmaz, Electric energy demand of

Turkey for the year 2050. Energy Sources, 2004. 26(12): p.

1157-1164.

30. Kavaklioglu, K., et al., Modeling and prediction of Turkey's

electricity consumption using Artificial Neural Networks.

Energy Conversion and Management, 2009. 50(11): p. 2719-

2727.

31. Toksari, M.D., A hybrid algorithm of Ant Colony

Optimization (ACO) and Iterated Local Search (ILS) for

estimating electricity domestic consumption: Case of Turkey.

International Journal of Electrical Power & Energy Systems,

2016. 78: p. 776-782.

building and cost analysis of an industrial automation

Documents